ArticleJournal of neuro-oncology2018
Diagnostic accuracy of MRI texture analysis for grading gliomas.
Article in Journal of neuro-oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 4 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.European radiology · 2021Pooled it
- Pooled it
- Pooled it
- Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study.European radiology · 2022Trial
- Hypoxia-targeted BOLD MRI signal heterogeneity as a complementary metric for glioblastoma characterization: a pilot study.Journal of neuro-oncology · 2026Article
- Machine learning based on radiomics for discriminating sellar region langerhans cell histiocytosis from germ cell tumors.Frontiers in pediatrics · 2026Article
- Spatial Mapping of Glioblastoma Infiltration: Diffusion Tensor Imaging-Based Radiomics and Connectomics in Recurrence Prediction.Brain sciences · 2025Review
- Grading of Gliomas by Contrast-Enhanced CT Radiomics Features.Journal of biomedical physics & engineering · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Article
- Diagnostic value of MRI radiomics in differentiating high‑grade glioma from low‑grade glioma: A meta‑analysis.Oncology letters · 2023Article
- Prediction of Recurrent Cervical Cancer in 2-Year Follow-Up After Treatment Based on Quantitative and Qualitative Magnetic Resonance Imaging Parameters: A Preliminary Study.Annals of surgical oncology · 2023Article
- Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model.Brain sciences · 2023Article
- Tumor volume and calcifications as indicators for preoperative differentiation of grade II/III diffuse gliomas.Journal of neuro-oncology · 2023Article
- The combination of radiomics features and VASARI standard to predict glioma grade.Frontiers in oncology · 2023Article
- Risk factors for the recurrence of cervical cancer using MR-based T1 mapping: A pilot study.Frontiers in oncology · 2023Article
- The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma.Translational cancer research · 2022Article
- MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke.BMC medical imaging · 2022Article
- Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion.Cancers · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeTexture analysis (TA) can quantify variations in surface intensity or patterns, including some that are imperceptible to the human visual system. The purpose of this study was to determine the diagnostic accuracy of radiomic based filtration-histogram TA to differentiate high-grade from low-grade gliomas by assessing tumor heterogeneity.
methodsPatients with a histopathological diagnosis of glioma and preoperative 3T MRI imaging were included in this retrospective study. A region of interest was manually delineated on post-contrast T1 images. TA was performed using commercially available research software. The histogram parameters including mean, standard deviation, entropy, mean of the positive pixels, skewness, and kurtosis were analyzed at spatial scaling factors ranging from 0 to 6 mm. The parameters were correlated with WHO glioma grade using Spearman correlation. Areas under the curve (AUC) were calculated using ROC curve analysis to distinguish tumor grades.
resultsOf a total of 94 patients, 14 had low-grade gliomas and 80 had high-grade gliomas. Mean, SD, MPP, entropy and kurtosis each showed significant differences between glioma grades for different spatial scaling filters. Low and high-grade gliomas were best-discriminated using mean of 2 mm fine texture scale, with a sensitivity and specificity of 93% and 86% (AUC of 0.90).
conclusionsQuantitative measurement of heterogeneity using TA can discriminate high versus low-grade gliomas. Radiomic data of texture features can provide complementary diagnostic information for gliomas.
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